Application of the Response Surface Methodology (RSM) in the Optimization of Acenaphthene (ACN) Removal from Wastewater by Activated Carbon

The presence of polycyclic aromatic hydrocarbons (PAHs) in wastewater has raised concerns about human health due to their potential carcinogenic and mutagenic properties. The widespread use of products containing acenaphthene (ACN, one of the 16 priority PAHs) in many industries and large-scale ACN release into the wastewater has resulted in dangerous concentrations of ACN in the environment. As a result, before discharge, it is required to eliminate or reduce its concentration to an acceptable level. Adsorption is an effective method of removing PAHs from wastewater. In this study, the ACN adsorption reaction in sample wastewater was evaluated using activated carbon produced by oil palm leaves. HPLC was used as an analytical method for quantifying ACN in wastewater samples. The initial concentration of ACN in water samples was 9.58 +/- 0.5 mg/L. The experiments were conducted using the CCD combined with the RSM and using three independent variables, i.e., pH, activated carbon dosage (g/L), and contact time (min), and one dependent variable, i.e., ACN removal efficiency (%). The ANOVA was used to identify the significance of the developed model in the RSM. Lastly, the RSM was used to optimize the adsorption results. The experimental results determined that the removal of 98.73 +/- 1% of ACN (the highest amount) was achieved at pH 7, while the removal of 88.44 +/- 1% of ACN (the lowest amount) was achieved at pH 4.5. The adsorption efficiency of ACN was slightly increased by an increase in activated carbon dosage from 0.1 to 3 g/L (<4%). The contact time was the most significant factor in controlling the adsorption efficiency of ACN in wastewater, and not pH value or dosage. The adsorption reaction was quick, and 88-90% of ACN was removed within 5 min of the adsorption reaction, followed by slower adsorption for up to 90 min. The RSM model was developed on the basis of experimental results. An ANOVA determined that the developed model was significant enough to represent the adsorption data as the p-value was <0.05 for the model. The factors pH, adsorbent dosage, and contact time were also significant factors (p-value < 0.05). The optimization results showed that pH of 6.96, adsorbent dosage of 2.62 g/L, and contact time of 71.67 min were the optimal conditions for eliminating 98.88% of the ACN. The optimization results were verified in the lab, and a close agreement was found between the predicted results of the RSM and experimental results. The study found that the RSM is an effective tool for optimizing operating variables, as well as for significantly reducing time and experimentation costs.